This was basically what always confounded me as well - it doesn't really make sense to begin with and yet they start with that foundation that makes no sense and run with it to create "humanization" plugins.jancivil wrote: Sun May 02, 2021 6:34 am Where is the logic, that makes a life of musical muscle memory and all the training that informs this, and the experience interacting closely with others and developing groove and sensitivity to time, in real time, with all of the memories and internal life that goes into a human musical expression, especially in the realm of timing, be captured in a pseudo-randomization scheme run by a machine with no actual agency?
I don't see algorithms as a way to do this at all, although I wouldn't want to scratch the possibility. I do think machine learning/AI - though it still wouldn't be perfect - could produce the closest result we could hope for at the moment, depending on the amount of data it has to process. Basically what it would be doing is creating probabilities based off "listening" to hundreds or thousands of hours of a performer, learning their "style" (or tendencies), and then projecting the application of that style onto a set of midi data. So the entire software is then based off of a type of "understanding" and projection of the human agency of that specific performer.
It does get super complex, and the "level 1" version of the above wouldn't be accounting for the variables you or I have mentioned - group playing, genre, etc. but would basically be a "solo" version to start with. With ML/AI what it comes down to is how much data you have - so if we took every recording available of John Bonham, extrapolated the drum parts, analyzed it with tensorflow and then applied the projections onto a new midi drum file, we'd have a sort of "approximation" of Bonhams playing, not accounting for a number of variables but perhaps capturing and then projecting some of the human agency he brings to his performances. If we have a living studio musician and put him with a variety of different combos (Jazz, Rock, etc.), record the data and specifically project his "tendencies" for each genre we would then have that additional data point and would be more accurate in relation to that musicians agency when playing a specific genres.
An expensive but effective way to make it more complete would be to create a type of "ensemble" for this and have a group of say 25 studio musicians that all play different genres of music with one another, of different tempi, different keys, and to record them together performing hundreds of hours of music, and then - after processing the data and having these now virtual musicians available to choose from by the end user - give the end user an ability to specify genre, mood, etc. The ML/AI now has hundreds of hours human agency to interpret and project and a variety of different data points, meaning if you added these "virtual" musicians to a midi rock piece and specify it as such you'd be essentially getting the most accurate portrayal of those musicians playing that music available at this time, and this could be optimized over decades of time by periodically adding more data from the same musicians (to an extent).
I think the benefit of the ML/AI tack is that it is specifically attempting to project based off of a type of statistical analysis of the actual human agency. So say if you have 1000 hours of a drummer playing. You take the first 500 hours of their playing, analyze them, then the analyzer attempts to predict through projection the final 500 hours and then adjusts it's interpretation on whether or not it accurately predicted that second 500 hours. So, with a consistent musician, you may end up with an AI model that with that first 500 hours of playing can predict that performers "moves" with say 80% accuracy on the final 500 hours. That model is then applied to music that is not pre-recorded with the understanding that the interpretation would then be around 80% accurate for those as well, though this may or may not be completely accurate.
The more data that's collected the more accurate the model will become. If you were to extrapolate the drum data from every rock album ever made and pump all of that drum data into it you'd end up with a type of "meta" rock drummer - a generalized extrapolated and projected spirit of all rock drumming that could then be applied - with limited accuracy - to midi data.
Again, not a perfect solution, but a solution that would bring us way closer IMO than applying a randomization algo, which again, IMO makes no sense. Another benefit to this approach is - because it becomes more accurate with more data - it can always be improved on and enhanced to become more accurate as long as the musicians are available to provide more data.
